Spatial Relationships and Driving Mechanisms of Ecosystem Health in Urbanization: A Case Study of Wuhan, China

Abstract

Rapid urbanization has markedly affected urban ecosystem health (EH), making it imperative to explore the relationships between EH and urbanization, as well as to identify the key factors influencing EH. This study addresses 2 key research gaps: (a) The traditional pressure–state–response evaluation framework fails to integrate ecosystem service demands and landscape pattern indices and has not formed a comprehensive EH evaluation system. (b) There is a lack of research on investigating the drivers and thresholds of EH across the areas in different spatial relationship between urbanization and EH at the urban scale. Here, taking Wuhan, China, as an example, this study assesses EH utilizing an optimized pressure–state–response evaluation framework. Additionally, bivariate Moran’s I is used to analyze the spatial relationship between EH and urbanization. We use gradient boosting decision trees to flexibly model the nonlinear relationships between influencing factors and EH, while Shapley additive explanations quantify each factor’s contribution, enhancing model interpretability and clarifying their effects on EH. The findings reveal a spatial distribution pattern characterized by lower EH levels in central areas and higher EH levels in periphery areas, with a notable negative spatial correlation between EH and urbanization. The spatial heterogeneity and clustering of EH and urbanization across Wuhan exhibit a ringlike pattern radiating from the center to the periphery. Landscape pattern index and land use are identified as key influencing factors of EH in Wuhan, with substantial regional variation, necessitating targeted environmental protection strategies. This study offers insights into urban planning and policymaking, promoting sustainable urban development.